OpenBioMed is an agent platform and toolkit collection for biomedical research and drug discovery, covering areas such as molecular design, protein analysis, and single-cell data analysis. It is intended for researchers and provides the biomedical skills listed in the catalogue as workflows for Claude Code.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PharMolix/OpenBioMed --skill single-cell-atac-seq-peak-calling-annotaiongit clone --depth 1 https://github.com/PharMolix/OpenBioMedWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pharmolix/openbiomed/single-cell-atac-seq-peak-calling-annotaion)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/single-cell-atac-seq-peak-calling-annotaion"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/single-cell-atac-seq-peak-calling-annotaion/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pharmolix/openbiomed/single-cell-atac-seq-peak-calling-annotaion"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/single-cell-atac-seq-peak-calling-annotaion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.03167 |
| Opus 5 | $0.00000 | $0.01584 |
| Sonnet 5 | $0.00000 | $0.00633 |
| Haiku 4.5 | $0.00000 | $0.00317 |
Grade A, and why
single-cell-atac-seq-peak-calling-annotaion scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ATAC-seq Peak Calling and Differential Accessibility
Call accessible chromatin peaks from ATAC-seq BAM files, annotate peaks to genomic features and genes, and identify differentially accessible regions between experimental conditions.
This is Step 2 of the bulk ATAC-seq pipeline.
What it does
- Calls peaks with MACS2 (
--nomodel --shift -100 --extsize 200) optimized for ATAC-seq - Filters peaks overlapping ENCODE blacklist regions
- Builds a consensus peak set across all samples using bedtools merge
- Counts reads per sample in consensus peaks (featureCounts or bedtools coverage)
- Annotates peaks with genomic features (promoter, UTR, exon, intron, intergenic) and nearest gene
- Runs differential accessibility analysis with DESeq2 on pseudobulk counts (recommended) or DiffBind
- Applies FDR correction and generates ranked DAR table and volcano plot
Why this exists
If you ask a general AI to "call peaks from ATAC-seq data," it will:
- Use default MACS2 parameters (designed for ChIP-seq), not ATAC-seq-specific parameters
- Not build a consensus peak set across samples, making cross-sample comparison impossible
- Not remove ENCODE blacklist regions, leaving artifactual peaks from repetitive elements
- Use
--format BAMinstead of--format BAMPE(ATAC-seq is paired-end) - Apply DESeq2 directly to individual reads per cell instead of pseudobulk aggregation for multi-sample data
This skill encodes the correct methodological decisions:
- Uses ATAC-seq-specific MACS2 flags:
--nomodel --shift -100 --extsize 200 --format BAMPE - Filters ENCODE blacklist regions (hg38 or mm10) that produce artifactual signal
- Builds a reproducible consensus peak set using IDR or bedtools merge across replicates
- Applies pseudobulk DESeq2 for multi-sample differential analysis (controls type I error)
Reference Methods
MACS2 ATAC-seq parameters:
--nomodel: Skip the ChIP enrichment model building (ATAC-seq does not have a broad enrichment model)--shift -100 --extsize 200: Centers signal on Tn5 cut site by shifting reads 100 bp upstream and extending 200 bp--format BAMPE: Reads paired-end fragment coordinates directly from BAM--nolambda: Disables local lambda background estimation (optional; use with high-coverage data)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 294 lines · 0 tokens per session scan A 5cf62fc2edce
single-cell-atac-seq-peak-calling-annotaion is a skill published in the GitHub repository PharMolix/OpenBioMed (1,105 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,167 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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